[1]朱晓东,任春晓,刘晓兰,等.基于适应度地形分析的优化算法调度方法[J].郑州大学学报(工学版),2025,46(06):32-39.[doi:10.13705/j.issn.1671-6833.2025.03.017]
　ZHU Xiaodong,REN Chunxiao,LIU Xiaolan,et al.Optimization Algorithm Scheduling Method Based on Fitness Landscape Analysis[J].Journal of Zhengzhou University (Engineering Science),2025,46(06):32-39.[doi:10.13705/j.issn.1671-6833.2025.03.017]
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基于适应度地形分析的优化算法调度方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年06期
页码:
32-39
栏目:
出版日期:
2025-10-25

文章信息/Info

Title:
Optimization Algorithm Scheduling Method Based on Fitness Landscape Analysis
文章编号:
1671-6833(2025)06-0032-08
作者:
朱晓东;  任春晓;  刘晓兰;  陈　科;  余春明
郑州大学 电气与信息工程学院,河南 郑州 450001
Author(s):
ZHU Xiaodong;  REN Chunxiao;  LIU Xiaolan;  CHEN Ke;  YU Chunming
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
关键词:
优化算法调度;  适应度地形;  特征提取;  局部峰值点;  哈里斯鹰优化算法;  差分进化算法
Keywords:
optimization algorithm scheduling;  fitness landscape analysis;  feature extraction;  local peak points;  harris hawks optimization algorithm;  differential evolution algorithm
分类号:
TP391TP301.4TP18
DOI:
10.13705/j.issn.1671-6833.2025.03.017
文献标志码:
A
摘要:
由于不同的优化问题具有不同的适应度地形,而一种优化算法通常只在某一种适应度地形上有更好的效果,因此,提出了一种基于适应度地形分析的优化算法调度方法(FL-AMAS)。首先,通过提取优化目标函数的局部峰簇数特征来描述优化问题的地形特征,根据地形特征选择相应具有优势的算法,利用对算法的调度发挥不同算法的最大优势;其次,根据优化问题对探索性与开发性的平衡要求,选择了具有高开发能力的哈里斯鹰优化算法(HHO)和具有高探索能力的差分进化算法(DE)作为调度使用的算法,根据不同的适应度地形特征来选择更适合的算法。实验结果表明:在基准测试集上,相较于单独使用HHO,FL-AMAS在收敛性能上提升了75%;与DE算法相比,FL-AMAS收敛性能提升了40%。将FL-AMAS与6种先进算法进行比较,在75%的基准测试集上,FL-AMAS的收敛精度均优于这些算法。通过调度其他类型优化算法的结果进行对比,也验证了所提调度方法的有效性和扩展性。
Abstract:
Optimization algorithms often perform optimally on specific types of fitness terrains due to the varying nature of optimization problems. To address this limitation, in this study an optimization algorithm scheduling method grounded in fitness terrain analysis was introduced. This method characterizes the terrain features of an optimization problem by extracting the local peak cluster number features of the optimization objective function. Based on these terrain features, the method selected the most suitable algorithm to maximize the advantages of different algorithms through effective scheduling. In particular, this study considered the balance between exploration and exploitation in optimization problems by selecting the harris hawks optimization algorithm (HHO), known for its high development capability, and the differential evolution algorithm (DE), recognized for its strong exploration ability, as the scheduling algorithms. The choice of algorithm was tailored to the specific adaptability characteristics of the terrain. Experimental results show that the convergence performance of FL-AMAS was improved by 75% compared with that of HHO alone, and by 40% compared with that of DE algorithm. Further, FL-AMAS was compared with six advanced algorithms, and FL-AMAS outperformed these algorithms in convergence accuracy on 75% of the benchmark set. The effectiveness and scalability of the proposed scheduling method were further validated through comparisons with other types of scheduling optimization algorithms.

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更新日期/Last Update: 2025-10-21